Identification of differentially co-expressed genes with lipid metabolism in Parkinson's disease by bioinformatics analysis

Jinxiao Song; Xinyao Wang; Lipeng Dong; Shuo Hui; Ziyu Huang; Ling Fan; Yongtao Zhu; Jingru Zhao

doi:10.1016/j.neuroscience.2025.06.053

Identification of differentially co-expressed genes with lipid metabolism in Parkinson's disease by bioinformatics analysis

Neuroscience. 2025 Jul 2:S0306-4522(25)00741-9. doi: 10.1016/j.neuroscience.2025.06.053. Online ahead of print.

Authors

Jinxiao Song¹, Xinyao Wang², Lipeng Dong³, Shuo Hui⁴, Ziyu Huang⁵, Ling Fan⁵, Yongtao Zhu⁵, Jingru Zhao⁶

Affiliations

¹ Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China; Department of Graduate School, Hebei Medical University, Shijiazhuang, Hebei, China; Department of Neurology, Pingshan People's Hospital, Pingshan, Shijiazhuang, Hebei, China.
² Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China; Department of Graduate School, North China University of Science and Technology, Tangshan, Hebei, China.
³ Biology Post-doctoral Research Stations, Hebei Normal University, Shijiazhuang, Hebei, China; Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China.
⁴ The Department of Neurology, Xi'an XD Group Hospital, Xi'an, Shanxi, China.
⁵ Biology Post-doctoral Research Stations, Hebei Normal University, Shijiazhuang, Hebei, China; Department of Graduate School, Hebei Medical University, Shijiazhuang, Hebei, China.
⁶ Biology Post-doctoral Research Stations, Hebei Normal University, Shijiazhuang, Hebei, China; Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China. Electronic address: zjr840911@163.com.

PMID: 40614927
DOI: 10.1016/j.neuroscience.2025.06.053

Abstract

There was increasing evidence that lipid metabolism disorders played a significant part in the maturation of Parkinson's disease (PD). The purpose of the article was to investigate a significance of lipid metabolism-related genes (LMRGs) in the maturation of PD. The function of LMRGs in the etiology of PD was explored by analysing PD datasets from Gene Expression Omnibus. First, Based on the internal training set, differentially expressed genes (DEGs) were obtained. Then, using weighted gene co-expression network analysis, 1083 module genes were screened. Intersected the genes obtained above, 430 overlapping genes were obtained, and further intersection operation was performed with 746 LMRGs, and finally 22 lipid metabolism-related Parkinson's disease (L-PD) characteristic genes were identified. These characteristic genes were mainly participated in many biological processes. Subsequently, seven core genes were screened by using CytoHubba plug-in and machine learning algorithm LASSO regression and random forest. Verified by internal and external data sets, two potential biomarkers were found to have high sensitivity and specificity. In addition, CIBERSORT immune cell infiltration analysis revealed that a variety of immune cells may be participated in the emergence and development of PD. Finally, the quantitative real-time polymerase chain reaction results showed that, The expression levels of PPARGC1A and SRD5A1 in blood samples could distinguish between PD and healthy controls, and could also distinguish patients with different Hoehn & Yahr stages. In summary, the discovery of PPARGC1A and SRD5A1 provided new targets and directions for L-PD mechanism research and drug development.

Keywords: Bioinformatics analysis; Immune infiltration; Lipid metabolism; Parkinson’s disease.